For years, the narrative around artificial intelligence has centered on GPUs (graphics processing units) and their compute power. Companies have readily embraced the idea that expensive, state-of-the-art GPUs are essential for training and running AI models. Public cloud providers and hardware manufacturers have promoted this belief, marketing newer, more powerful chips as crucial for remaining competitive in the race for AI innovation.
The surprising truth? GPUs were never as critical to enterprise AI success as we were led to believe. Many of the AI workloads enterprises depend on today, such as recommendation engines, predictive analytics, and chatbots, don’t require access to the most advanced hardware. Older GPUs or even commodity CPUs can often suffice at a fraction of the cost.
As pressure mounts to cut costs and boost efficiency, companies are questioning the hype around GPUs and finding a more pragmatic way forward, changing how they approach AI infrastructure and investments.
A dramatic drop in GPU prices
Recent reports reveal that the prices of cloud-delivered, high-demand GPUs have plummeted. For example, the cost of an AWS H100 GPU Spot Instance dropped by as much as 88% in some regions, down from $105.20 in early 2024 to $12.16 by late 2025. Similar price declines have been seen across all major cloud providers.
This decline may seem positive. Businesses save money, and cloud providers adjust supply. However, there’s a critical shift in business decision-making behind these numbers. The price cuts did not result from an oversupply; they reflect changing priorities. Demand for top-tier GPUs is falling as enterprises question why they should pay for expensive GPUs when more affordable alternatives offer nearly identical results for most AI workloads.
Not all AI requires high-end GPUs
The idea that bigger and better GPUs are essential for AI’s success has always been flawed. Sure, training large models like GPT-4 or MidJourney needs a lot of computing power, including top-tier GPUs or TPUs. But these cases account for a tiny share of AI workloads in the business world. Most businesses focus on AI inference tasks that use pretrained models for real-world applications: sorting emails, making purchase recommendations, detecting anomalies, and generating customer support responses. These tasks do not require cutting-edge GPUs. In fact, many inference jobs run perfectly on slightly older GPUs such as Nvidia’s A100 or H100 series, which are now available at a much lower cost.
Even more surprising? Some companies find they don’t need GPUs at all for many AI-related operations. Standard commodity CPUs can handle smaller, less complex models without issue. A chatbot for internal HR inquiries or a system designed to forecast energy consumption doesn’t require the same hardware as a groundbreaking AI research project. Many companies are realizing that sticking to expensive GPUs is more about prestige than necessity.
When AI became the next big thing, it came with skyrocketing hardware requirements. Companies rushed to get the latest GPUs to stay competitive, and cloud providers were happy to help. The problem? Many of these decisions were driven by hype and fear of missing out (FOMO) rather than thoughtful planning. Laurent Gil, CEO of Cast AI, noted how customer behavior is driven by FOMO when buying new GPUs.
As economic pressures rise, many enterprises are realizing that they’ve been overprovisioning their AI infrastructure for years. ChatGPT was built on older Nvidia GPUs and performed well enough to set AI benchmarks. If major innovations could succeed without the latest hardware, why should enterprises insist on it for far simpler tasks? It’s time to reassess hardware choices and determine whether they align with actual workloads. Increasingly, the answer is no.
Public cloud providers adapt
This shift is evident in cloud providers’ inventories. High-end GPUs like Nvidia’s GB200 Blackwell processors remain in extremely short supply, and that’s not going to change anytime soon. Meanwhile, older models such as the A100 sit idle in data centers as companies pull back from buying the next big thing.
Many providers likely overestimated demand, assuming enterprises would always want newer, faster chips. In reality, companies now focus more on cost efficiency than innovation. Spot pricing has further aggravated these market dynamics, as enterprises use AI-driven workload automation to hunt for the cheapest available options.
Gil also explained that enterprises willing to shift workloads dynamically can save up to 80% compared to those locked into static pricing agreements. This level of agility wasn’t plausible for many companies in the past, but with self-adjusting systems increasingly available, it’s now becoming the standard.
A paradigm of common sense
Expensive, cutting-edge GPUs may remain a critical tool for AI innovation at the bleeding edge, but for most businesses, the path to AI success is paved with older GPUs or even commodity CPUs. The decline in cloud GPU prices shows that more companies realize AI doesn’t require top-tier hardware for most applications. The market correction from overhyped, overprovisioned conditions now emphasizes ROI. This is a healthy and necessary correction to the AI industry’s unsustainable trajectory of overpromising and overprovisioning.
If there’s one takeaway, it’s that enterprises should invest where it matters: pragmatic solutions that deliver business value without breaking the bank. At its core, AI has never been about hardware. Companies should focus on delivering insights, generating efficiencies, and improving decision-making. Success lies in how enterprises use AI, not in the hardware that fuels it. For enterprises hoping to thrive in the AI-driven future, it’s time to ditch outdated assumptions and embrace a smarter approach to infrastructure investments.
